Learning to Adapt Domain Shifts of Moral Values via Instance Weighting
Classifying moral values in user-generated text from social media is critical in understanding community cultures and interpreting user behaviors of social movements. Moral values and language usage can change across the social movements; however, text classifiers are usually trained in source domains of existing social movements and tested in target domains of new social issues without considering the variations. In this study, we examine domain shifts of moral values and language usage, quanti- doi
- 10.1145/3511095.3531269
- name
- Learning to Adapt Domain Shifts of Moral Values via Instance Weighting
- source
- ACM HyperText 2022 proceedings PDF (authorized direct conversion)
- license
- © 2022 Copyright held by the owner/author(s). Publication rights licensed to ACM.
- summary
- Classifying moral values in user-generated text from social media is critical in understanding community cultures and interpreting user behaviors of social movements. Moral values and language usage can change across the social movements; however, text classifiers are usually trained in source domains of existing social movements and tested in target domains of new social issues without considering the variations. In this study, we examine domain shifts of moral values and language usage, quanti
- import_kind
- full_text
- open_access
- false
- displayAuthor
- Xiaolei Huang, Alexandra Wormley, Adam Cohen
- displayPublishTime
- 2022-06-28
- acm_source_attribution
- Converted directly from authorized ACM proceedings PDF
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